The influence of communication structure on opinion dynamics in social networks with multiple true states

In order to study the influence of communication structure on opinion dynamics in social networks with multiple true states, a social learning model is proposed in which the social network is composed of strongly connected communities and some uninformed individuals outside the communities. Multiple communities correspond to multiple underlying true states. At each time step, the individual in a community receives his private signal and updates his opinion to be a weighted linear combination of his own Bayesian posterior opinion and the opinions of his neighbors, while the uninformed individual outside the communities has no private signal and only takes the neighbors’ weighted average opinion as his own opinion. Simulation results show that in the social network environment with multiple true states, for the uninformed individual, the opinion will depend on the weighted average opinion of his neighbors, and for the individual in the strongly connected community, the opinion dynamics will depend on the communication types between communities. If communities are not connected, he will achieve asymptotic learning. If communities are unidirectionally connected, he will learn the true state of the root community. If communities are bidirectionally connected, his opinion will appear chaos phenomenon and oscillate within a certain range. So, in a society with multiple true states, the absence of communication between communities is conducive to asymptotic learning and bidirectional communications between communities will hinder asymptotic learning.